Analyze AFM tip solvation¶

This notebook demonstrates deposition of an SDS adsorption layer on a non-spherical AFM tip model.

Initialization¶

IPython magic¶

%load_ext autoreload
%autoreload 2

%aimport

Modules to reload:
all-except-skipped

Modules to skip:

Imports¶

import ase.io # here used for reading pdb files
from ase.visualize import view
from ase.visualize.plot import plot_atoms # has nasty offset issues
from cycler import cycler # here used for cycling through colors in plots
import datetime
import fabric # for pythonic ssh connections
from fireworks import LaunchPad, Firework, Tracker, Workflow 
from fireworks import FileTransferTask, PyTask, ScriptTask

# FireWorks functionality 
from fireworks import Firework, LaunchPad, ScriptTask, Workflow
from fireworks.user_objects.firetasks.templatewriter_task import TemplateWriterTask
from fireworks.user_objects.firetasks.filepad_tasks import AddFilesTask, GetFilesTask, GetFilesByQueryTask
from imteksimfw.fireworks.user_objects.firetasks.cmd_tasks import CmdTask
from fireworks.utilities.filepad import FilePad # direct FilePad access, similar to the familiar LaunchPad

import glob
import gc # manually clean up memory with gc.collect()
import gromacs # GromacsWrapper, here used for evoking gmc commands, reading and writing .ndx files
# from io import StringIO, TextIOWrapper
import io
from IPython.display import display, Image #, Video # display image files within notebook
from ipywidgets import Video  # display video within notebook
import itertools # for products of iterables
import json # generic serialization of lists and dicts
import jinja2 # here used for filling packmol input script template
import jinja2.meta # for gathering variables in a jinja2 template
import logging 
import matplotlib.pyplot as plt
import MDAnalysis as mda # here used for reading and analyzing gromacs trajectories
import MDAnalysis.analysis.rdf as mda_rdf
import MDAnalysis.analysis.rms as mda_rms
from mpl_toolkits.mplot3d import Axes3D # here used for 3d point cloud scatter plot
import miniball # finds minimum bounding sphere of a point set
import nglview
import numpy as np
import os, os.path
import pandas as pd
import panedr # reads GROMACS edr into pandas df, requires pandas and pbr
import parmed as pmd # has quite a few advantages over ASE when it comes to parsing pdb
from pprint import pprint
import pymongo # for sorting in queries
import scipy.constants as sc
import subprocess # used for evoking external packmol
import sys
import tempfile
import yaml

NOTE: Some configuration directories are not set up yet: 
	/home/centos/.gromacswrapper
	/home/centos/.gromacswrapper/qscripts
	/home/centos/.gromacswrapper/templates
NOTE: You can create the configuration file and directories with:
	>>> import gromacs
	>>> gromacs.config.setup()

GromacsWrapper might need a file ~/.gromacswrapper.cfg with content

[Gromacs]
tools = gmx gmx_d 
# gmx_mpi_d gmx_mpi_d

# name of the logfile that is written to the current directory
logfilename = gromacs.log

# loglevels (see Python's logging module for details)
#   ERROR   only fatal errors
#   WARN    only warnings
#   INFO    interesting messages
#   DEBUG   everything

# console messages written to screen
loglevel_console = INFO

# file messages written to logfilename
loglevel_file = DEBUG

in order to know the GROMACS executables it is allowed to use. Otherwise, calls to gmx_mpi or gmx_mpi_d without MPI wrapper might lead to MPI warnings in output that cause GromacsWrapper to fail.

Logging¶

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.INFO)

ParmEd needs to know the GROMACS topology folder, usually get this from envionment variable GMXLIB:

Function definitions¶

def find_undeclared_variables(infile):
    """identify all variables evaluated in a jinja 2 template file"""
    env = jinja2.Environment()
    with open(infile) as template_file:
        parsed = env.parse(template_file.read())

    undefined = jinja2.meta.find_undeclared_variables(parsed)
    return undefined

def memuse():
    """Quick overview on memory usage of objects in Jupyter notebook"""
    # https://stackoverflow.com/questions/40993626/list-memory-usage-in-ipython-and-jupyter
    # These are the usual ipython objects, including this one you are creating
    ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars']

    # Get a sorted list of the objects and their sizes
    return sorted([(x, sys.getsizeof(globals().get(x))) for x in dir(sys.modules['__main__']) if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)

Global settings¶

# pandas settings
pd.options.display.max_rows = 200
pd.options.display.max_columns = 16
pd.options.display.max_colwidth = 256

os.environ['GMXLIB'] = '/gmx_top'

# pmd.gromacs.GROMACS_TOPDIR = os.environ['GMXLIB']
pmd.gromacs.GROMACS_TOPDIR = '/gmx_top'

# prefix = '/mnt/dat/work/testuser/indenter/sandbox/20191110_packmol'
prefix = '/mnt/dat/work'

work_prefix = '/mnt/dat/work/tmp'

try:
    os.mkdir(work_prefix)
except FileExistsError as exc:
    print(exc)

[Errno 17] File exists: '/mnt/dat/work/tmp'

os.chdir(work_prefix)

# the FireWorks LaunchPad
lp = LaunchPad.auto_load() #Define the server and database
# FilePad behaves analogous to LaunchPad
fp = FilePad.auto_load()

Conversion from LAMMPS data format to PDB¶

The vollowing bash / tcl snippet converts a LAMMPS data file to PDB, assigning the desired names as mapped in a yaml file

#!/bin/bash
# echo "package require jlhvmd; jlh lmp2pdb indenter.lammps indenter.pdb" | vmd -eofexit
vmd -eofexit << 'EOF'
package require jlhvmd
topo readlammpsdata indenter.lammps
jlh type2name SDS_type2name.yaml
jlh name2res  SDS_name2res.yaml
set sel [atomselect top all]
$sel writepdb indenter.pdb
EOF

pdb_chain.py indenter.pdb > indenter_wo_chainid.pdb
pdb_reres_by_atom_9999.py indenter_wo_chainid.pdb > indenter_reres.pdb

Requires

VMD (tested with 1.9.3) with topotools
jlhvmd VMD plugin: https://github.com/jotelha/jlhvmd
pdb-tools: https://github.com/haddocking/pdb-tools/

Overview¶

Overview on projects in database¶

query = {'metadata.datetime': {'$gt': '2020'} }

fp.filepad.count_documents(query)

3075

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 'project': '$metadata.project' },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # pull 'project' field up in hierarchy
        "$addFields": { 
            "project": "$_id.project",
        },
    },
    {  # drop nested '_id.project'
        "$project": { 
            "_id": False 
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

# sort_aggregation
#aggregation_pipeline = [ match_aggregation, group_aggregation, set_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [c for c in cursor]
res_df = pd.DataFrame(data=res) # pandas Dataframe is just nice for printing in notebook

res_df

Overview on steps in project¶

project_id = '2020-07-09-passivation'

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1398

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

res_df['step'].values

array(['ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching: GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad',
       'PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad',
       'SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad',
       'IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad',
       'ParametricBranching:GromacsEnergyMinimizationAfterSolvation:GromacsEnergyMinimizationAfterSolvationMain:push_infiles',
       'ParametricBranching:GromacsNVTEquilibration:GromacsNVTEquilibrationMain:push_infiles',
       'IndenterBoundingSphere:IndenterBoundingSphereMain:push_infiles',
       'ParametricBranching:SphericalSurfactantPacking:push_infiles',
       'ParametricBranching:GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles',
       'ParametricBranching:GromacsPullPrep:GromacsPullPrepMain:push_infiles',
       'SurfactantMoleculeMeasures:SurfactantMoleculeMeasuresMain:push_infiles',
       'ParametricBranching:GromacsEnergyMinimization:GromacsEnergyMinimizationMain:push_infiles',
       'ParametricBranching:GromacsNPTEquilibration:GromacsNPTEquilibrationMain:push_infiles'],
      dtype=object)

Overview on objects in project¶

project_id = '2020-07-09-passivation'

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1398

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'step', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Overview on images by distinct steps¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

15

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

res_df["step"][0]

'ParametricBranching:SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad'

Packing visualization¶

Indenter bounding sphere¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'IndenterBoundingSphere'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

for obj in obj_list:
    display(obj)

Surfactant measures¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'SurfactantMoleculeMeasures'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

obj_list[0]

Packing constraints¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'PackingConstraintSpheres'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

obj_list[0]

Packed film¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'SphericalSurfactantPacking'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

............

for obj in obj_list:
    display(obj)

Energy minimization analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

228

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',  #{'$regex': 'GromacsEnergyMinimization'}
    "metadata.type": 'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f0674307dc9cfbf449a2729'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f0694b57dc9cfbf449adff5'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f0663417dc9cfbf4499dfb5'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f068a927dc9cfbf449aab63'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f0664207dc9cfbf4499e0c4'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f06649b7dc9cfbf4499e1d0'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f0680527dc9cfbf449a6f39'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f0667507dc9cfbf4499e4b5'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f066a657dc9cfbf4499ec88'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f067fd97dc9cfbf449a6e88'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f0689cc7dc9cfbf449aaa1e'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f068a0a7dc9cfbf449aaac7'}]

res_mi_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        em_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),em_df.index],
            names=[*c["_id"].keys(),'step'])
        em_mi_df = em_df.set_index(mi)        
        res_mi_list.append(em_mi_df)
    print('.',end='')
print('')

res_mi_df = pd.concat(res_mi_list)
res_df = res_mi_df.reset_index()

............

res_mi_df

res_df

y_quantities = [
    'Potential',
    'Pressure',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

positions = [
    (0,0),
    (0,1),
    (1,0),
    (2,0),
    (2,1),
]
fig, ax = plt.subplots(3,2,figsize=(10,12))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Trajectory¶

query = { 
    "metadata.project": project_id,
    "metadata.step": 'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":  { '$in': ['trajectory_file','data_file'] },
}
fp.filepad.count_documents(query)

24

# Building a rather sophisticated aggregation pipeline

# first group by nmolecules and type ...

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules',
    'type': 'metadata.type'}
# parameter_names = ['nmolecules', 'type']

query = { 
    "metadata.project": project_id,
    "metadata.step": 'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":  { '$in': ['trajectory_file','data_file'] },
}

aggregation_pipeline = []

aggregation_pipeline.append({ 
    "$match": query
})

aggregation_pipeline.append({ 
    "$sort": { 
        "metadata.system.surfactant.nmolecules": pymongo.ASCENDING,
        "metadata.datetime": pymongo.DESCENDING,
    }
})

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
})

# second group by nmolecules

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { field: '$_id.{}'.format(field) for field in parameter_dict.keys() },
        "type":     {"$addToSet": "$_id.type"},
        "gfs_id":   {"$addToSet": "$latest"} 
        #"$_id.type": "$latest"
    }
})

aggregation_pipeline.append({
    '$project': {
         '_id': False,
        **{field: '$_id.{}'.format(field) for field in parameter_dict.keys()},
        'objects': { 
            '$zip': {
                'inputs': [ '$type', '$gfs_id' ],
                'useLongestLength': True,
                'defaults':  [None,None]
            }
        }
    }
})

aggregation_pipeline.append({ 
    '$project': {
        **{p: True for p in parameter_dict.keys()},
        'objects': {'$arrayToObject': '$objects'}
        #'objects': False 
    }
})

aggregation_pipeline.append({ 
    '$addFields': {
        'objects': {**{ field: '${}'.format(field) for field in parameter_dict.keys()}}
    }
})

aggregation_pipeline.append({ 
    '$replaceRoot': { 'newRoot': '$objects' }
})

# display results with
# for i, c in enumerate(cursor): 
#    print(c)
# yields documents in the form
# {'em_gro': '5e6a4e3d6c26f976ceae5e38', 'em_trr': '5e6a4e3a6c26f976ceae5e14', 'nmolecules': '44'}
# i.e. most recent topology file and trajectory file per concentration

cursor = fp.filepad.aggregate(aggregation_pipeline)

mda_trr_list = []
for i, c in enumerate(cursor): 
    em_gro_content, _ = fp.get_file_by_id(c["data_file"])
    em_trr_content, _ = fp.get_file_by_id(c["trajectory_file"])
    # Stream approach won't work
    # with io.TextIOWrapper( io.BytesIO(em_gro_content) ) as gro, \
    #    io.BytesIO(em_trr_content) as trr:   
        #mda_trr_list.append( 
        #    mda.Universe( 
        #        gro,trr, topology_format = 'GRO', format='TRR') )
    with tempfile.NamedTemporaryFile(suffix='.gro') as gro, \
        tempfile.NamedTemporaryFile(suffix='.trr') as trr:
        gro.write(em_gro_content)
        trr.write(em_trr_content)
        mda_trr_list.append( mda.Universe(gro.name,trr.name) )
    print('.',end='')
print('')
del em_gro_content
del em_trr_content

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
/usr/local/lib/python3.8/dist-packages/MDAnalysis/core/universe.py in _topology_from_file_like(topology_file, topology_format, **kwargs)
    123         with parser(topology_file) as p:
--> 124             topology = p.parse(**kwargs)
    125     except (IOError, OSError) as err:

/usr/local/lib/python3.8/dist-packages/MDAnalysis/topology/GROParser.py in parse(self, **kwargs)
     87         # resid, resname, name, index, (x,y,z)
---> 88         with openany(self.filename) as inf:
     89             next(inf)

/usr/lib/python3.8/contextlib.py in __enter__(self)
    112         try:
--> 113             return next(self.gen)
    114         except StopIteration:

/usr/local/lib/python3.8/dist-packages/MDAnalysis/lib/util.py in openany(datasource, mode, reset)
    311     """
--> 312     stream = anyopen(datasource, mode=mode, reset=reset)
    313     try:

/usr/local/lib/python3.8/dist-packages/MDAnalysis/lib/util.py in anyopen(datasource, mode, reset)
    398                 openfunc = handlers[ext]
--> 399                 stream = _get_stream(datasource, openfunc, mode=mode)
    400                 if stream is not None:

/usr/local/lib/python3.8/dist-packages/MDAnalysis/lib/util.py in _get_stream(filename, openfunction, mode)
    445         try:
--> 446             stream.readline()
    447         except IOError:

/usr/lib/python3.8/codecs.py in decode(self, input, final)
    321         data = self.buffer + input
--> 322         (result, consumed) = self._buffer_decode(data, self.errors, final)
    323         # keep undecoded input until the next call

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9 in position 3: invalid continuation byte

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-89-f7d9705d8d67> in <module>
    104         gro.write(em_gro_content)
    105         trr.write(em_trr_content)
--> 106         mda_trr_list.append( mda.Universe(gro.name,trr.name) )
    107     print('.',end='')
    108 print('')

/usr/local/lib/python3.8/dist-packages/MDAnalysis/core/universe.py in __init__(self, *args, **kwargs)
    368         if not isinstance(topology, Topology) and not topology is None:
    369             self.filename = _check_file_like(topology)
--> 370             topology = _topology_from_file_like(self.filename,
    371                                                 topology_format=topology_format,
    372                                                 **kwargs)

/usr/local/lib/python3.8/dist-packages/MDAnalysis/core/universe.py in _topology_from_file_like(topology_file, topology_format, **kwargs)
    137                             "Error: {2}".format(topology_file, parser, err))
    138     except (ValueError, NotImplementedError) as err:
--> 139         raise ValueError(
    140             "Failed to construct topology from file {0}"
    141             " with parser {1}.\n"

ValueError: Failed to construct topology from file /tmp/tmp155aolvb.gro with parser <class 'MDAnalysis.topology.GROParser.GROParser'>.
Error: 'utf-8' codec can't decode byte 0xc9 in position 3: invalid continuation byte

mda_trr = mda_trr_list[0]

mda_view = nglview.show_mdanalysis(mda_trr)

mda_view.clear_representations()
mda_view.background = 'white'
mda_view.add_representation('ball+stick')
mda_view

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-86-e925056007c4> in <module>
----> 1 mda_trr = mda_trr_list[0]
      2 
      3 mda_view = nglview.show_mdanalysis(mda_trr)
      4 
      5 mda_view.clear_representations()

IndexError: list index out of range

Rendered videos¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_list.append(Video.from_file(tmp.name))
    print('.',end='')

............

obj_list[-1]

Pulling analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

276

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

The gmx energy table:

  1  Restraint-Pot.   2  U-B              3  Proper-Dih.      4  LJ-14         
  5  Coulomb-14       6  LJ-(SR)          7  Coulomb-(SR)     8  Coul.-recip.  
  9  Position-Rest.  10  COM-Pull-En.    11  Potential       12  Kinetic-En.   
 13  Total-Energy    14  Temperature     15  Pressure        16  Constr.-rmsd  
 17  Vir-XX          18  Vir-XY          19  Vir-XZ          20  Vir-YX        
 21  Vir-YY          22  Vir-YZ          23  Vir-ZX          24  Vir-ZY        
 25  Vir-ZZ          26  Pres-XX         27  Pres-XY         28  Pres-XZ       
 29  Pres-YX         30  Pres-YY         31  Pres-YZ         32  Pres-ZX       
 33  Pres-ZY         34  Pres-ZZ         35  #Surf*SurfTen   36  T-rest

converted to dict with regex

 \s+([0-9]+)\s+([^\s]+)

and replacement

 '$2': $1,\n

gmx_energy_dict = {
    'Restraint-Pot.': 1,
    'U-B': 2,
    'Proper-Dih.': 3,
    'LJ-14': 4,
    'Coulomb-14': 5,
    'LJ-(SR)': 6,
    'Coulomb-(SR)': 7,
    'Coul.-recip.': 8,
    'Position-Rest.': 9,
    'COM-Pull-En.': 10,
    'Potential': 11,
    'Kinetic-En.': 12,
    'Total-Energy': 13,
    'Temperature': 14,
    'Pressure': 15,
    'Constr.-rmsd': 16,
    'Vir-XX': 17,
    'Vir-XY': 18,
    'Vir-XZ': 19,
    'Vir-YX': 20,
    'Vir-YY': 21,
    'Vir-YZ': 22,
    'Vir-ZX': 23,
    'Vir-ZY': 24,
    'Vir-ZZ': 25,
    'Pres-XX': 26,
    'Pres-XY': 27,
    'Pres-XZ': 28,
    'Pres-YX': 29,
    'Pres-YY': 30,
    'Pres-YZ': 31,
    'Pres-ZX': 32,
    'Pres-ZY': 33,
    'Pres-ZZ': 34,
    '#Surf*SurfTen': 35,
    'T-rest': 36,
}

query = { 
    "metadata.project": project_id,
    "metadata.type":    'energy_file',
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

gmx_energy_selection = [
    'Restraint-Pot.',
    'Position-Rest.',
    'COM-Pull-En.',
    'Potential',
    'Kinetic-En.',
    'Total-Energy',
    'Temperature',
    'Pressure',
    'Constr.-rmsd',
]

res_list = []
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

res_df_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    
    #df = panedr.edr_to_df(tmp.name), fails
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.edr', delete=False)
    
    # cur_res_dict = {}
    with tmpin:
        tmpin.write(content)
        #tmpin.seek(0)
       
    res_df = None
    for sel in gmx_energy_selection:  
        try:
            tmpout = tempfile.NamedTemporaryFile(suffix='.xvg', delete=False)
            res = gromacs.energy(f=tmpin.name,o=tmpout.name,
                                 input=str(gmx_energy_dict[sel]))
            #with open(tmpout.name,'r') as f:
            #    xvg = f.read()
            #tmpout.delete()
            xvg = mda.auxiliary.XVG.XVGReader(tmpout.name)
            xvg_time = xvg.read_all_times()
            xvg_data = np.array([ f.data[1:] for f in xvg ]).flatten() # 1st entry contains times
            os.unlink(tmpout.name)
        except: 
            logger.warning("Failed to read '{:s}' from data set {:d}.".format(sel,i))
            failed_list.append((nmolecules, sel))
        else:
            r = {'nmolecules': [nmolecules]*len(xvg_time), 'time': xvg_time, sel: xvg_data}
            cur_df = pd.DataFrame(r)
            if res_df is None:
                res_df = cur_df
            else:
                res_df = pd.merge(res_df, cur_df, how='outer', on=['nmolecules', 'time'])
    res_df_list.append(res_df)
    os.unlink(tmpin.name)
    print('.',end='')
print('')
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])

............

res_df_mi

cols = 2
y_quantities = [
    'Restraint-Pot.',
    'Position-Rest.',
    'COM-Pull-En.',
    'Potential',
    'Kinetic-En.',
    'Total-Energy',
    'Temperature',
    'Pressure',
    'Constr.-rmsd',
    ]
n = len(y_quantities)
rows = round(n/cols)
positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Pulling forces¶

res_df_list = []
failed_list = []

query = { 
    "metadata.project": project_id,
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
    "metadata.type": 'pullf_file',
}

fp.filepad.count_documents(query)
match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.xvg', delete=False)
    
    with tmpin:
        tmpin.write(content)
        
    try:
        xvg = mda.auxiliary.XVG.XVGReader(tmpin.name)
        xvg_time = xvg.read_all_times()
        xvg_data = np.array([ f.data[1:] for f in xvg ])# .flatten() # 1st entry contains times
    except: 
        logger.warning("Failed to read data set {:d}.".format(i))
        failed_list.append(nmolecules)
    else:
        res_df_list.append(pd.DataFrame({
            'nmolecules': np.array([nmolecules]*len(xvg_time), dtype=int),
            'time': xvg_time, 
            **{i: xvg_data[:,i] for i in range(nmolecules)}
        }))
    os.unlink(tmpin.name)
    print('.',end='')
print('')
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])

............

n = len(res_df['nmolecules'].unique())
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for pos, (key, grp) in zip(positions,res_df.groupby(['nmolecules'])):
    columns = list(set(grp.columns) - set(['nmolecules','time']))
    grp.plot('time', columns, ax=ax[pos],title=key,legend=None)
fig.tight_layout()

fig, ax = plt.subplots(1,1,figsize=(5,4))
for key, grp in res_df.groupby(['nmolecules']):
    columns = list(set(grp.columns) - set(['nmolecules','time']))
    grp = grp.set_index('time')
    grp = grp.drop(columns='nmolecules')
    grp.mean(axis=1).plot(legend=True, label=key, ax=ax)
fig.tight_layout()
#fig.legend()

Pulling groups movement¶

res_df_list = []
failed_list = []

query = { 
    "metadata.project": project_id,
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
    "metadata.type":    'pullx_file',
}

fp.filepad.count_documents(query)
match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)


for i, c in enumerate(cursor): 
    print(c["_id"])
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])  # int(metadata["metadata"]["nmolecules"])
    
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.xvg', delete=False)
    
    with tmpin:
        tmpin.write(content)
        
    try:
        xvg = gromacs.fileformats.XVG(tmpin.name)
        xvg_time = xvg.array[0,:]
        
        #xvg_labels = ['1', '1 ref', '1 dX', '1 dY', '1 dZ', '1 g 1 X', '1 g 1 Y', '1 g 1 Z', '1 g 2 X', '1 g 2 Y', '1 g 2 Z']
        N_pull_coords = nmolecules
        N_cols = len(xvg.names)
        N_cols_per_coord = int(N_cols / N_pull_coords)
        
        xvg_labels = xvg.names[:N_cols_per_coord]
        xvg_data = {}
        for j in range(N_pull_coords):
            for k in range(N_cols_per_coord):
                xvg_data[(j,xvg_labels[k])] = xvg.array[
                    1+j*N_cols_per_coord+k,:]
        
    except: 
        logger.exception("Failed to read data set {:d}.".format(i))
        failed_list.append(nmolecules)

    else:
        # res_list.append({
        #    'nmolecules': nmolecules, # np.array([nmolecules]*len(xvg_time), dtype=int),
        #    'time': xvg_time, 
        #    **xvg_data})
        res_df_list.append(pd.DataFrame({
            'nmolecules': np.array([nmolecules]*len(xvg_time), dtype=int),
            'time': xvg_time, 
            **xvg_data # {i: xvg_data[:,i] for i in range(nmolecules)}
        }))
    #os.unlink(tmpin.name)
    #print('.',end='')
#print('')
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])
res_df_mi.columns = pd.MultiIndex.from_tuples(res_df_mi.columns, names=['nmolecule', 'coord'])

{'nmolecules': 306}
{'nmolecules': 44}
{'nmolecules': 525}
{'nmolecules': 88}
{'nmolecules': 481}
{'nmolecules': 438}
{'nmolecules': 219}
{'nmolecules': 394}
{'nmolecules': 350}
{'nmolecules': 263}
{'nmolecules': 175}
{'nmolecules': 131}

res_df_mi

res_df = res_df_mi.groupby(axis=1,level='coord').mean().reset_index()

res_df

cols = 2
y_quantities = [
    '1', 
    '1 ref', 
    '1 dX', 
    '1 dY', 
    '1 dZ', 
    '1 g 1 X', 
    '1 g 1 Y', 
    '1 g 1 Z', 
    '1 g 2 X', 
    '1 g 2 Y', 
    '1 g 2 Z'
]
n = len(y_quantities)
rows = round(n/cols)
positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules',
    'type': 'metadata.type'}

query = { 
    "metadata.project": project_id,
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
    "metadata.type": { '$in': ['trajectory_file','data_file'] },
}

aggregation_pipeline = []

aggregation_pipeline.append({ 
    "$match": query
})



aggregation_pipeline.append({ 
    "$sort": { 
        "metadata.system.surfactant.nmolecules": pymongo.ASCENDING,
        "metadata.datetime": pymongo.DESCENDING,
    }
})

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
})

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { p: '$_id.{}'.format(p) for p in parameter_dict.keys() },
        "type":     {"$addToSet": "$_id.type"},
        "gfs_id":   {"$addToSet": "$latest"} 
        #"$_id.type": "$latest"
    }
})

aggregation_pipeline.append({
    '$project': {
         '_id': False,
        **{ p: '$_id.{}'.format(p) for p in parameter_dict.keys()},
        'objects': { 
            '$zip': {
                'inputs': [ '$type', '$gfs_id' ],
                'useLongestLength': True,
                'defaults':  [None,None]
            }
        }
    }
})

aggregation_pipeline.append({ 
    '$project': {
        **{ p: True for p in parameter_dict.keys()},
        'objects': {'$arrayToObject': '$objects'}
        #'objects': False 
    }
})

aggregation_pipeline.append({ 
    '$addFields': {
        'objects': {**{ p: '${}'.format(p) for p in parameter_dict.keys()}}
    }
})

aggregation_pipeline.append({ 
    '$replaceRoot': { 'newRoot': '$objects' }
})

# display results with
# for i, c in enumerate(cursor): 
#    print(c)
# yields documents in the form
# {'em_gro': '5e6a4e3d6c26f976ceae5e38', 'em_trr': '5e6a4e3a6c26f976ceae5e14', 'nmolecules': '44'}
# i.e. most recent topology file and trajectory file per concentration

cursor = fp.filepad.aggregate(aggregation_pipeline)

failed_list = []
mda_trr_list = []
for i, c in enumerate(cursor): 
    try:
        gro_content, _ = fp.get_file_by_id(c["data_file"])
        trr_content, _ = fp.get_file_by_id(c["trajectory_file"])
        with tempfile.NamedTemporaryFile(suffix='.gro') as gro, \
            tempfile.NamedTemporaryFile(suffix='.trr') as trr:
            gro.write(gro_content)
            trr.write(trr_content)
            mda_trr_list.append( mda.Universe(gro.name,trr.name) )
    except: 
        logger.exception("Failed to read data set {}.".format(c))
        failed_list.append(c)
    print('.',end='')
print('')

.

/home/jotelha/venv/jlh-imtek-nb-python-3.6/lib/python3.6/site-packages/MDAnalysis/topology/guessers.py:80: UserWarning: Failed to guess the mass for the following atom types: A
  warnings.warn("Failed to guess the mass for the following atom types: {}".format(atom_type))

failed_list

[]

mda_trr = mda_trr_list[0]

mda_view = nglview.show_mdanalysis(mda_trr)

mda_view.clear_representations()
mda_view.background = 'white'
mda_view.add_representation('ball+stick')
mda_view

/home/jotelha/venv/jlh-imtek-nb-python-3.6/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'altLocs' Using default value of ' '
  "".format(attrname, default))
/home/jotelha/venv/jlh-imtek-nb-python-3.6/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'icodes' Using default value of ' '
  "".format(attrname, default))
/home/jotelha/venv/jlh-imtek-nb-python-3.6/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'occupancies' Using default value of '1.0'
  "".format(attrname, default))
/home/jotelha/venv/jlh-imtek-nb-python-3.6/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'tempfactors' Using default value of '0.0'
  "".format(attrname, default))

Rendered videos¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_list.append(Video.from_file(tmp.name))
    print('.',end='')

............

for obj in obj_list:
    display(obj)

MSD & RDF¶

# reuse previous aggregation pipeline
cursor = fp.filepad.aggregate(aggregation_pipeline)

failed_list = []
# mda_trr_list = []
rms_substrate_list = []
rms_surfactant_head_list = []
rdf_substrate_headgroup_list = []


for i, c in enumerate(cursor): 
    try:
        gro_content, _ = fp.get_file_by_id(c["data_file"])
        trr_content, _ = fp.get_file_by_id(c["trajectory_file"])
        with tempfile.NamedTemporaryFile(suffix='.gro') as gro, \
            tempfile.NamedTemporaryFile(suffix='.trr') as trr:
            gro.write(gro_content)
            trr.write(trr_content)
            
            mda_trr = mda.Universe(gro.name,trr.name)
            substrate = mda_trr.atoms[mda_trr.atoms.names == 'AU']
            surfactant_head = mda_trr.atoms[mda_trr.atoms.names == 'S']
            
            rms_substrate = mda_rms.RMSD(substrate,ref_frame=0)
            rms_substrate.run()
            
            rms_surfactant_head = mda_rms.RMSD(surfactant_head,ref_frame=0)
            rms_surfactant_head.run()
            
            rdf_substrate_headgroup = mda_rdf.InterRDF(
                substrate,surfactant_head,range=(0.0,80.0),verbose=True)
            
            bins = []
            rdf  = []
            for i in range(len(mda_trr.trajectory)):
                rdf_substrate_headgroup = mda_rdf.InterRDF(
                    substrate,surfactant_head,range=(0.0,80.0),verbose=True)
                rdf_substrate_headgroup.run(start=i,stop=i+1)
                bins.append(rdf_substrate_headgroup.bins.copy())
                rdf.append(rdf_substrate_headgroup.rdf.copy())
            bins = np.array(bins)
            rdf = np.array(rdf)
            
            rms_substrate_list.append(rms_substrate)
            rms_surfactant_head_list.append(rms_surfactant_head)
            rdf_substrate_headgroup_list.append(rms_surfactant_head)
            
    except: 
        logger.exception("Failed to read data set {}.".format(c))
        failed_list.append(c)
    print('.',end='')
print('')

ERROR:root:Failed to read data set {'_id': {'nmolecules': 481}, 'degeneracy': 1, 'latest': '5eb586e1c35b0391733091ec'}.
Traceback (most recent call last):
  File "<ipython-input-208-72d38b5c47ad>", line 13, in <module>
    gro_content, _ = fp.get_file_by_id(c["data_file"])
KeyError: 'data_file'
ERROR:root:Failed to read data set {'_id': {'nmolecules': 394}, 'degeneracy': 1, 'latest': '5eb5e260c61194c216bd5858'}.
Traceback (most recent call last):
  File "<ipython-input-208-72d38b5c47ad>", line 13, in <module>
    gro_content, _ = fp.get_file_by_id(c["data_file"])
KeyError: 'data_file'

..

Substrate MSD (none)¶

rmsd = rms_substrate_list[0].rmsd.T   # transpose makes it easier for plotting
time = rmsd[1]

rdf_substrate_headgroup.n_frames

1

plt.plot(time,rmsd[2])

[<matplotlib.lines.Line2D at 0x7f3344246668>]

Surfactant head MSD¶

rmsd = rms_surfactant_head_list[0].rmsd.T   # transpose makes it easier for plotting
time = rmsd[1]

plt.plot(time,rmsd[2])

[<matplotlib.lines.Line2D at 0x7f334e652f98>]

Au-S (substrate - head group ) RDF¶

# indicates desired approach towards substrate
plt.plot(bins[0],rdf[0],label="Initial RDF")
plt.plot(bins[3],rdf[4],label="Intermediat RDF")
plt.plot(bins[-1],rdf[-1],label="Final RDF")
plt.legend()

<matplotlib.legend.Legend at 0x7f334f234a58>

Pre-evaluated RDF¶

Overview¶

query = { 
    "metadata.project": project_id,
    "metadata.type": {'$regex': '.*rdf$'},
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

120

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Substrate - surfactant head RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_head_rdf',
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

cols = 2
n = len(res_dict)
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_tail_rdf',
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

cols = 2
n = len(res_dict)
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_head_surfactant_tail_rdf',
    "metadata.step": "ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

cols = 2
n = len(res_dict)
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Energy minimization after solvation analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

240

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',  #{'$regex': 'GromacsEnergyMinimization'}
    "metadata.type": 'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res_mi_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        em_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),em_df.index],
            names=[*c["_id"].keys(),'step'])
        em_mi_df = em_df.set_index(mi)        
        res_mi_list.append(em_mi_df)
    print('.',end='')
print('')

res_mi_df = pd.concat(res_mi_list)
res_df = res_mi_df.reset_index()

............

res_mi_df

y_quantities = [
    'Potential',
    'Pressure',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

positions = [
    (0,0),
    (0,1),
    (1,0),
    (2,0),
    (2,1),
]
fig, ax = plt.subplots(3,2,figsize=(10,12))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Rendered videos¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'nmolecules': '$metadata.system.surfactant.nmolecules'
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

# for i, c in enumerate(cursor): 
#    content, metadata = fp.get_file_by_id(c["latest"])
#    nmolecules = int(c["_id"]["nmolecules"])
    

res = [ {**c['_id'], **c} for c in cursor]
columns = ['nmolecules', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_dict.update({metadata['metadata']['system']['surfactant']['nmolecules']: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

Trajectory¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    { '$in': ['trajectory_file','data_file'] },
}
fp.filepad.count_documents(query)

2

# Building a rather sophisticated aggregation pipeline

# first group by nmolecules and type ...

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules',
    'type': 'metadata.type'}
# parameter_names = ['nmolecules', 'type']

query = { 
    "metadata.project": project_id,
    "metadata.type":    { '$in': ['trajectory_file','data_file'] },
}

aggregation_pipeline = []

aggregation_pipeline.append({ 
    "$match": query
})

aggregation_pipeline.append({ 
    "$sort": { 
        "metadata.system.surfactant.nmolecules": pymongo.ASCENDING,
        "metadata.datetime": pymongo.DESCENDING,
    }
})

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
})

# second group by nmolecules

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline.append({ 
    "$group": { 
        "_id": { field: '$_id.{}'.format(field) for field in parameter_dict.keys() },
        "type":     {"$addToSet": "$_id.type"},
        "gfs_id":   {"$addToSet": "$latest"} 
        #"$_id.type": "$latest"
    }
})

aggregation_pipeline.append({
    '$project': {
         '_id': False,
        **{field: '$_id.{}'.format(field) for field in parameter_dict.keys()},
        'objects': { 
            '$zip': {
                'inputs': [ '$type', '$gfs_id' ],
                'useLongestLength': True,
                'defaults':  [None,None]
            }
        }
    }
})

aggregation_pipeline.append({ 
    '$project': {
        **{p: True for p in parameter_dict.keys()},
        'objects': {'$arrayToObject': '$objects'}
        #'objects': False 
    }
})

aggregation_pipeline.append({ 
    '$addFields': {
        'objects': {**{ field: '${}'.format(field) for field in parameter_dict.keys()}}
    }
})

aggregation_pipeline.append({ 
    '$replaceRoot': { 'newRoot': '$objects' }
})

# display results with
# for i, c in enumerate(cursor): 
#    print(c)
# yields documents in the form
# {'em_solvated_gro': '5e6a4e3d6c26f976ceae5e38', 'em_solvated_trr': '5e6a4e3a6c26f976ceae5e14', 'nmolecules': '44'}
# i.e. most recent topology file and trajectory file per concentration

cursor = fp.filepad.aggregate(aggregation_pipeline)

mda_trr_list = []
for i, c in enumerate(cursor): 
    em_gro_content, _ = fp.get_file_by_id(c["data_file"])
    em_trr_content, _ = fp.get_file_by_id(c["trajectory_file"])
    # Stream approach won't work
    # with io.TextIOWrapper( io.BytesIO(em_gro_content) ) as gro, \
    #    io.BytesIO(em_trr_content) as trr:   
        #mda_trr_list.append( 
        #    mda.Universe( 
        #        gro,trr, topology_format = 'GRO', format='TRR') )
    with tempfile.NamedTemporaryFile(suffix='.gro') as gro, \
        tempfile.NamedTemporaryFile(suffix='.trr') as trr:
        gro.write(em_gro_content)
        trr.write(em_trr_content)
        mda_trr_list.append( mda.Universe(gro.name,trr.name) )
    print('.',end='')
print('')

.

mda_trr = mda_trr_list[0]
# check unique resiude names in system
resnames = np.unique([ r.resname for r in mda_trr.residues ])

resnames

array(['AUM', 'NA', 'SDS', 'SOL'], dtype='<U3')

mda_view = nglview.show_mdanalysis(mda_trr)

# setSize: https://cloud.githubusercontent.com/assets/22888066/21761120/e47e2988-d68b-11e6-9e11-a894d7833d50.png
mda_view._remote_call('setSize', target='Widget', args=['600px','600px'])

mda_view.clear_representations()
mda_view.background = 'white'
mda_view.add_representation(repr_type='ball+stick',selection='SDS')
mda_view.add_representation(repr_type='ball+stick',selection='NA')
mda_view.add_representation(repr_type='spacefill',selection='AUM',color='yellow')
mda_view.center()
mda_view

Render images¶

len(mda_trr.trajectory)

101

# https://ambermd.org/tutorials/analysis/tutorial_notebooks/nglview_movie/index.html
    
# make sure to change your web browser option to save files as default (vs open file by external program)
# NGLView will render each snapshot and save image to your web browser default download location
# uncomment all the commands below to render

from time import sleep

# # to save time for this tutorial, we make a movie with only 50 frames
for frame in range(0, len(mda_trr.trajectory)):
     # set frame to update coordinates
    mda_view.frame = frame
    # make sure to let NGL spending enough time to update coordinates
    sleep(0.5)
    mda_view.download_image(filename='frame{:04d}.png'.format(frame))
    # make sure to let NGL spending enough time to render before going to next frame
    sleep(2.0)

# ffmpeg -r 20 -f image2 -i frame%04d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p out.mp4

Render gif¶

from nglview.contrib.movie import MovieMaker

output_folder = os.path.join(work_prefix,'mov')

try:
    os.mkdir(output_folder)
except FileExistsError as exc:
    print(exc)

mov = MovieMaker(
    mda_view, download_folder=output_folder, output="em_solvated.gif")

mov.make()

Make avi¶

# write avi format
from nglview.contrib.movie import MovieMaker
moviepy_params = {
     'codec': 'mpeg4'
}
movie = MovieMaker(mda_view, output='em_solvated.avi', in_memory=True, moviepy_params=moviepy_params)
movie.make()

Again, relax the system a little with positional constraints applied to all ions.

NVT equilibration analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

252

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f06a4727dc9cfbf449b454c'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f0675437dc9cfbf449a27e8'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f0681007dc9cfbf449a6fed'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f06733a7dc9cfbf449a1a5f'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f068e847dc9cfbf449aad05'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f0696ee7dc9cfbf449af9c9'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f06728e7dc9cfbf449a0db0'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f068fb07dc9cfbf449ac64e'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f0670de7dc9cfbf449a00f8'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f0697ed7dc9cfbf449b0662'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f069f8f7dc9cfbf449b38e6'},
 {'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f06885f7dc9cfbf449a9735'}]

res_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        res_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),res_df.index],
            names=[*c["_id"].keys(),'step'])
        res_mi_df = res_df.set_index(mi)
        res_list.append(res_mi_df)
    print('.',end='')
print('')
res_df_mi = pd.concat(res_list)
res_df = res_df_mi.reset_index()

............

res_df.columns

Index(['nmolecules', 'step', 'Time', 'Bond', 'U-B', 'Proper Dih.', 'LJ-14',
       'Coulomb-14', 'LJ (SR)', 'Coulomb (SR)', 'Coul. recip.', 'Potential',
       'Kinetic En.', 'Total Energy', 'Conserved En.', 'Temperature',
       'Pressure', 'Constr. rmsd', 'Vir-XX', 'Vir-XY', 'Vir-XZ', 'Vir-YX',
       'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY', 'Vir-ZZ', 'Pres-XX', 'Pres-XY',
       'Pres-XZ', 'Pres-YX', 'Pres-YY', 'Pres-YZ', 'Pres-ZX', 'Pres-ZY',
       'Pres-ZZ', '#Surf*SurfTen', 'T-non-Substrate', 'T-Substrate',
       'Lamb-non-Substrate', 'Lamb-Substrate'],
      dtype='object')

res_df_mi

#n = len(res_df['nmolecules'].unique())
y_quantities = [
    'Temperature',
    'Pressure',
    'Potential',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

n = len(y_quantities)
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(394,)

(438,)

(350,)

(263,)

(175,)

(131,)

(44,)

(306,)

(88,)

NPT equilibration analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

252

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f0696577dc9cfbf449aed24'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f0679a77dc9cfbf449a4e02'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f069d3f7dc9cfbf449b1995'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f067ec17dc9cfbf449a61d4'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f0687b67dc9cfbf449a8a84'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f0695c77dc9cfbf449ae077'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f069e257dc9cfbf449b2c56'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f06a5d57dc9cfbf449b51aa'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f06aabf7dc9cfbf449b5e11'},
 {'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f068f1d7dc9cfbf449ab9b0'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f0677cb7dc9cfbf449a3499'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f0679007dc9cfbf449a4152'}]

res_list = []

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        res_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),res_df.index],
            names=[*c["_id"].keys(),'step'])
        res_mi_df = res_df.set_index(mi)
        res_list.append(res_mi_df)
    print('.',end='')
print('')
res_df_mi = pd.concat(res_list)
res_df = res_df_mi.reset_index()

............

res_df.columns

Index(['nmolecules', 'step', 'Time', 'Bond', 'U-B', 'Proper Dih.', 'LJ-14',
       'Coulomb-14', 'LJ (SR)', 'Coulomb (SR)', 'Coul. recip.', 'Potential',
       'Kinetic En.', 'Total Energy', 'Conserved En.', 'Temperature',
       'Pressure', 'Constr. rmsd', 'Box-X', 'Box-Y', 'Box-Z', 'Volume',
       'Density', 'pV', 'Enthalpy', 'Vir-XX', 'Vir-XY', 'Vir-XZ', 'Vir-YX',
       'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY', 'Vir-ZZ', 'Pres-XX', 'Pres-XY',
       'Pres-XZ', 'Pres-YX', 'Pres-YY', 'Pres-YZ', 'Pres-ZX', 'Pres-ZY',
       'Pres-ZZ', '#Surf*SurfTen', 'Box-Vel-XX', 'Box-Vel-YY', 'Box-Vel-ZZ',
       'Coul-SR:Surfactant-Surfactant', 'LJ-SR:Surfactant-Surfactant',
       'Coul-14:Surfactant-Surfactant', 'LJ-14:Surfactant-Surfactant',
       'Coul-SR:Surfactant-Substrate', 'LJ-SR:Surfactant-Substrate',
       'Coul-14:Surfactant-Substrate', 'LJ-14:Surfactant-Substrate',
       'Coul-SR:Surfactant-Ion', 'LJ-SR:Surfactant-Ion',
       'Coul-14:Surfactant-Ion', 'LJ-14:Surfactant-Ion',
       'Coul-SR:Surfactant-SOL', 'LJ-SR:Surfactant-SOL',
       'Coul-14:Surfactant-SOL', 'LJ-14:Surfactant-SOL',
       'Coul-SR:Substrate-Substrate', 'LJ-SR:Substrate-Substrate',
       'Coul-14:Substrate-Substrate', 'LJ-14:Substrate-Substrate',
       'Coul-SR:Substrate-Ion', 'LJ-SR:Substrate-Ion', 'Coul-14:Substrate-Ion',
       'LJ-14:Substrate-Ion', 'Coul-SR:Substrate-SOL', 'LJ-SR:Substrate-SOL',
       'Coul-14:Substrate-SOL', 'LJ-14:Substrate-SOL', 'Coul-SR:Ion-Ion',
       'LJ-SR:Ion-Ion', 'Coul-14:Ion-Ion', 'LJ-14:Ion-Ion', 'Coul-SR:Ion-SOL',
       'LJ-SR:Ion-SOL', 'Coul-14:Ion-SOL', 'LJ-14:Ion-SOL', 'Coul-SR:SOL-SOL',
       'LJ-SR:SOL-SOL', 'Coul-14:SOL-SOL', 'LJ-14:SOL-SOL', 'T-non-Substrate',
       'T-Substrate', 'Lamb-non-Substrate', 'Lamb-Substrate'],
      dtype='object')

res_df_mi

#n = len(res_df['nmolecules'].unique())
y_quantities = [
    'Temperature',
    'Pressure',
    'Volume',
    'Potential',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

n = len(y_quantities)
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(175,)

(394,)

(350,)

(438,)

(263,)

(131,)

(88,)

(44,)

(306,)

(481,)

Global observables¶

Visualize trajectory¶

mda_trr = mda.Universe('nvt.gro','npt.trr')

/opt/apps/mdtools/jlh-11Jul19/lib/python3.6/site-packages/MDAnalysis/topology/guessers.py:80: UserWarning: Failed to guess the mass for the following atom types: A
  warnings.warn("Failed to guess the mass for the following atom types: {}".format(atom_type))

# check unique resiude names in system
resnames = np.unique([ r.resname for r in mda_trr.residues ])

resnames

array(['AUM', 'NA', 'SDS', 'SOL'], dtype='<U3')

mda_view = nglview.show_mdanalysis(mda_trr)
mda_view.clear_representations()
mda_view.background = 'white'
mda_view.add_representation(repr_type='ball+stick',selection='SDS')
mda_view.add_representation(repr_type='ball+stick',selection='NA')
mda_view.add_representation(repr_type='spacefill',selection='AUM',color='yellow')
mda_view.center()

/opt/apps/mdtools/jlh-11Jul19/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'altLocs' Using default value of ' '
  "".format(attrname, default))
/opt/apps/mdtools/jlh-11Jul19/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'icodes' Using default value of ' '
  "".format(attrname, default))
/opt/apps/mdtools/jlh-11Jul19/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'occupancies' Using default value of '1.0'
  "".format(attrname, default))
/opt/apps/mdtools/jlh-11Jul19/lib/python3.6/site-packages/MDAnalysis/coordinates/PDB.py:916: UserWarning: Found no information for attr: 'tempfactors' Using default value of '0.0'
  "".format(attrname, default))

mda_view

substrate = mda_trr.select_atoms('resname AUM')

substrate.masses= ase.data.atomic_masses[ase.data.atomic_numbers['Au']]

substrtate_com_traj = np.array([substrate.center_of_mass() for ts in mda_trr.trajectory ])

substrtate_rgyr_traj = np.array([substrate.radius_of_gyration() for ts in mda_trr.trajectory ])

fig = plt.figure(figsize=(12,10))
ax = fig.add_subplot(111, projection='3d',azim=-30)
ax.plot(*substrtate_com_traj.T)
ax.scatter(*substrtate_com_traj[0,:],color='green')
ax.scatter(*substrtate_com_traj[-1,:],color='red')

<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fbf150d8b70>

plt.plot(substrtate_rgyr_traj)

[<matplotlib.lines.Line2D at 0x7fbf14917a90>]

try:
    del mda_trr
except:
    pass
try:
    del mda_view
except:
    pass

	object_count	earliest	latest	project
0	1386	2020-07-09 01:30:15.276530	2020-07-09 01:30:16.819138	2020-07-09-passivation
1	350	2020-07-08 13:31:27.106684	2020-07-08 14:02:41.994152	2020-07-08-passiv-trial-revisited
2	236	2020-07-02 19:37:19.035754	2020-07-02 19:37:19.770123	2020-07-03-passiv-trial-revisited
3	54	2020-07-02 03:49:00.103034	2020-07-02 03:49:00.325450	2020-07-01-passiv-trial-revisited
4	6	2020-05-11 15:34:17.973141	2020-05-11 15:34:18.054752	2020-05-11-2nd-passiv-trial
5	64	2020-05-11 00:46:35.427907	2020-05-11 00:46:35.637375	2020-05-11-passiv-trial
6	76	2020-05-09 23:23:58.405636	2020-05-09 23:23:58.629348	2020-05-10-passiv-trial
7	54	2020-05-09 18:06:29.244729	2020-05-09 18:06:29.470488	2020-05-09-parametric
8	276	2020-05-08 17:40:43.151560	2020-05-08 20:09:19.785964	2020-05-08-final
9	8	2020-05-08 16:46:54.125267	2020-05-08 16:46:55.061579	2020-05-08-dtool-trial
10	102	2020-05-06 19:57:34.090873	2020-05-06 23:02:33.696573	2020-05-06-indenter-passivation-trial
11	4	2020-05-04 19:56:27.458671	2020-05-04 19:56:27.458686	2020-05-04-gmx-em-dtool-trial
12	1	2020-05-04 17:47:46.398832	2020-05-04 17:47:46.398832	2020-04-29-gmx-nvt-trial
13	15	2020-04-29 20:03:40.694070	2020-04-29 21:43:41.499280	2020-04-23-gmx-nvt-trial
14	102	2020-04-23 00:42:50.738462	2020-04-23 12:29:04.927501	2020-04-23-indenter-passivation-trial
15	6	2020-04-22 23:52:50.724373	2020-04-22 23:52:50.724774	2020-04-22-gmx-nvt-trial
16	30	2020-04-22 20:12:52.975107	2020-04-22 20:12:52.992097	2020-04-22-intermediate-trial
17	12	2020-04-22 15:35:33.694291	2020-04-22 19:37:27.812809	2020-04-22-trajectory-rendering-trial
18	23	2020-04-22 00:22:22.239748	2020-04-22 00:22:22.241080	2020-04-21-gmx-chain-wf-trial
19	2	2020-04-21 21:08:51.961011	2020-04-21 21:08:51.961024	2020-04-21-gmx-solvate-trial
20	42	2020-04-21 17:33:16.919815	2020-04-21 23:19:49.893920	2020-04-21-intermediate-trial
21	7	2020-04-21 15:35:02.223005	2020-04-21 15:35:02.223026	2020-04-21-gmx-pull-trial
22	15	2020-04-15 12:12:58.569894	2020-04-21 14:39:06.617272	2020-04-15-gmx-pull-prep-trial
23	74	2020-04-15 01:58:21.918222	2020-04-21 13:38:43.462681	2020-04-15-intermediate-trial
24	4	2020-04-15 00:28:04.783910	2020-04-15 00:28:04.783995	2020-04-15-gmx-em-trial
25	3	2020-04-14 21:19:31.298223	2020-04-14 21:19:31.298232	2020-04-14-gmx-prep-trial
26	2	2020-04-14 17:14:15.144625	2020-04-14 20:23:20.587799	2020-04-14-packmol-trial
27	2	2020-04-03 01:25:09.142195	2020-04-07 16:18:42.232568	2020-04-02-surfactant-molecule-measures-trial
28	5	2020-04-02 21:18:13.804918	2020-04-12 20:56:30.319559	2020-03-31-local-trial
29	39	2020-03-25 12:11:11.906332	2020-03-25 22:47:26.604903	juwels-pull-2020-03-17
30	8	2020-03-13 12:33:31.922163	2020-03-13 12:33:31.922497	juwels-afm-probe-solvation-trial-a-2020-03-13
31	10	2020-03-12 12:31:29.329732	2020-03-12 15:55:54.013161	juwels-gromacs-em-2020-03-12
32	42	2020-03-11 18:45:59.775161	2020-03-12 00:15:43.047626	juwels-gromacs-prep-2020-03-11
33	15	2020-03-09 18:13:09.455387	2020-03-09 23:30:45.006483	juwels-packmol-2020-03-09

	step	earliest	latest	object_count
0	ParametricBranching:GromacsNPTEquilibration:Pr...	2020-07-09 01:30:15.580991	2020-07-09 01:30:16.819138	252
1	ParametricBranching:GromacsNVTEquilibration:Pr...	2020-07-09 01:30:15.549133	2020-07-09 01:30:16.808797	252
2	ParametricBranching:GromacsEnergyMinimizationA...	2020-07-09 01:30:15.524515	2020-07-09 01:30:16.798142	240
3	ParametricBranching: GromacsSolvate:ProcessAna...	2020-07-09 01:30:15.516945	2020-07-09 01:30:16.790365	24
4	ParametricBranching:GromacsPull:ProcessAnalyze...	2020-07-09 01:30:15.514319	2020-07-09 01:30:16.787594	276
5	ParametricBranching:GromacsPullPrep:ProcessAna...	2020-07-09 01:30:15.496033	2020-07-09 01:30:16.779845	48
6	ParametricBranching:GromacsEnergyMinimization:...	2020-07-09 01:30:15.493208	2020-07-09 01:30:16.776401	228
7	ParametricBranching:GromacsPrep:ProcessAnalyze...	2020-07-09 01:30:15.485505	2020-07-09 01:30:16.768394	36
8	ParametricBranching:SphericalSurfactantPacking...	2020-07-09 01:30:15.477057	2020-07-09 01:30:16.759762	24
9	PackingConstraintSpheres:ProcessAnalyzeAndVisu...	2020-07-09 01:30:15.359415	2020-07-09 01:30:15.359422	2
10	SurfactantMoleculeMeasures:ProcessAnalyzeAndVi...	2020-07-09 01:30:15.326050	2020-07-09 01:30:15.326057	2
11	IndenterBoundingSphere:ProcessAnalyzeAndVisual...	2020-07-09 01:30:15.276530	2020-07-09 01:30:15.276541	2
12	ParametricBranching:SphericalSurfactantPacking...	None	None	2
13	ParametricBranching:GromacsEnergyMinimization:...	None	None	2
14	ParametricBranching:GromacsPullPrep:GromacsPul...	None	None	2
15	SurfactantMoleculeMeasures:SurfactantMoleculeM...	None	None	1
16	ParametricBranching:GromacsEnergyMinimizationA...	None	None	1
17	ParametricBranching:GromacsEnergyMinimization:...	None	None	1
18	ParametricBranching:GromacsNPTEquilibration:Gr...	None	None	1
19	ParametricBranching:GromacsNVTEquilibration:Gr...	None	None	1
20	IndenterBoundingSphere:IndenterBoundingSphereM...	None	None	1

	type	step	name	earliest	latest	object_count
0	mp4_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581056	2020-07-09 01:30:16.819138	12
1	surfactant_tail_rmsd	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581053	2020-07-09 01:30:16.819135	12
2	surfactant_head_rmsd	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581050	2020-07-09 01:30:16.819133	12
3	substrate_rmsd	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581047	2020-07-09 01:30:16.819130	12
4	counterion_rmsd	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581043	2020-07-09 01:30:16.819127	12
5	surfactant_tail_surfactant_tail_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581040	2020-07-09 01:30:16.819124	12
6	surfactant_head_surfactant_tail_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581037	2020-07-09 01:30:16.819122	12
7	surfactant_head_surfactant_head_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581034	2020-07-09 01:30:16.819119	12
8	substrate_surfactant_tail_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581031	2020-07-09 01:30:16.819117	12
9	substrate_surfactant_head_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581028	2020-07-09 01:30:16.819114	12
10	substrate_substrate_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581025	2020-07-09 01:30:16.819112	12
11	counterion_surfactant_tail_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581022	2020-07-09 01:30:16.819110	12
12	counterion_surfactant_head_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581019	2020-07-09 01:30:16.819107	12
13	counterion_substrate_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581016	2020-07-09 01:30:16.819105	12
14	counterion_counterion_rdf	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581013	2020-07-09 01:30:16.819102	12
15	index_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581010	2020-07-09 01:30:16.819100	12
16	topology_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581007	2020-07-09 01:30:16.819097	12
17	data_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581004	2020-07-09 01:30:16.819095	12
18	trajectory_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.581001	2020-07-09 01:30:16.819092	12
19	energy_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.580998	2020-07-09 01:30:16.819089	12
20	log_file	ParametricBranching:GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.580991	2020-07-09 01:30:16.819083	12
21	mp4_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549197	2020-07-09 01:30:16.808797	12
22	surfactant_tail_rmsd	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549193	2020-07-09 01:30:16.808794	12
23	surfactant_head_rmsd	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549190	2020-07-09 01:30:16.808792	12
24	substrate_rmsd	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549187	2020-07-09 01:30:16.808789	12
25	counterion_rmsd	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549184	2020-07-09 01:30:16.808787	12
26	surfactant_tail_surfactant_tail_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549181	2020-07-09 01:30:16.808784	12
27	surfactant_head_surfactant_tail_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549178	2020-07-09 01:30:16.808782	12
28	surfactant_head_surfactant_head_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549175	2020-07-09 01:30:16.808780	12
29	substrate_surfactant_tail_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549172	2020-07-09 01:30:16.808777	12
30	substrate_surfactant_head_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549169	2020-07-09 01:30:16.808775	12
31	substrate_substrate_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549166	2020-07-09 01:30:16.808772	12
32	counterion_surfactant_tail_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549163	2020-07-09 01:30:16.808770	12
33	counterion_surfactant_head_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549160	2020-07-09 01:30:16.808767	12
34	counterion_substrate_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549157	2020-07-09 01:30:16.808764	12
35	counterion_counterion_rdf	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549154	2020-07-09 01:30:16.808762	12
36	index_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549151	2020-07-09 01:30:16.808759	12
37	topology_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549148	2020-07-09 01:30:16.808757	12
38	data_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549145	2020-07-09 01:30:16.808754	12
39	trajectory_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549142	2020-07-09 01:30:16.808752	12
40	energy_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549139	2020-07-09 01:30:16.808749	12
41	log_file	ParametricBranching:GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.549133	2020-07-09 01:30:16.808743	12
42	mp4_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524564	2020-07-09 01:30:16.798142	12
43	surfactant_tail_rmsd	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524562	2020-07-09 01:30:16.798140	12
44	surfactant_head_rmsd	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524559	2020-07-09 01:30:16.798137	12
45	substrate_rmsd	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524556	2020-07-09 01:30:16.798135	12
46	counterion_rmsd	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524554	2020-07-09 01:30:16.798132	12
47	surfactant_tail_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524552	2020-07-09 01:30:16.798130	12
48	surfactant_head_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524549	2020-07-09 01:30:16.798127	12
49	surfactant_head_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524547	2020-07-09 01:30:16.798125	12
50	substrate_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524544	2020-07-09 01:30:16.798123	12
51	substrate_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524542	2020-07-09 01:30:16.798120	12
52	substrate_substrate_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524540	2020-07-09 01:30:16.798118	12
53	counterion_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524537	2020-07-09 01:30:16.798115	12
54	counterion_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524535	2020-07-09 01:30:16.798113	12
55	counterion_substrate_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524532	2020-07-09 01:30:16.798110	12
56	counterion_counterion_rdf	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524530	2020-07-09 01:30:16.798108	12
57	topology_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524527	2020-07-09 01:30:16.798105	12
58	data_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524525	2020-07-09 01:30:16.798102	12
59	trajectory_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524523	2020-07-09 01:30:16.798100	12
60	energy_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524520	2020-07-09 01:30:16.798097	12
61	log_file	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.524515	2020-07-09 01:30:16.798092	12
62	topology_file	ParametricBranching: GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.516949	2020-07-09 01:30:16.790365	12
63	data_file	ParametricBranching: GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.516945	2020-07-09 01:30:16.790360	12
64	mp4_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514376	2020-07-09 01:30:16.787594	12
65	surfactant_tail_rmsd	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514373	2020-07-09 01:30:16.787591	12
66	surfactant_head_rmsd	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514371	2020-07-09 01:30:16.787589	12
67	substrate_rmsd	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514369	2020-07-09 01:30:16.787587	12
68	counterion_rmsd	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514366	2020-07-09 01:30:16.787584	12
69	surfactant_tail_surfactant_tail_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514364	2020-07-09 01:30:16.787582	12
70	surfactant_head_surfactant_tail_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514361	2020-07-09 01:30:16.787579	12
71	surfactant_head_surfactant_head_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514359	2020-07-09 01:30:16.787577	12
72	substrate_surfactant_tail_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514356	2020-07-09 01:30:16.787574	12
73	substrate_surfactant_head_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514354	2020-07-09 01:30:16.787572	12
74	substrate_substrate_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514351	2020-07-09 01:30:16.787569	12
75	counterion_surfactant_tail_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514349	2020-07-09 01:30:16.787567	12
76	counterion_surfactant_head_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514346	2020-07-09 01:30:16.787565	12
77	counterion_substrate_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514344	2020-07-09 01:30:16.787562	12
78	counterion_counterion_rdf	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514342	2020-07-09 01:30:16.787560	12
79	topology_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514339	2020-07-09 01:30:16.787557	12
80	pullx_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514337	2020-07-09 01:30:16.787554	12
81	pullf_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514334	2020-07-09 01:30:16.787552	12
82	data_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514332	2020-07-09 01:30:16.787549	12
83	compressed_trajectory_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514329	2020-07-09 01:30:16.787547	12
84	trajectory_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514327	2020-07-09 01:30:16.787544	12
85	energy_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514324	2020-07-09 01:30:16.787525	12
86	log_file	ParametricBranching:GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.514319	2020-07-09 01:30:16.787520	12
87	input_file	ParametricBranching:GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.496042	2020-07-09 01:30:16.779845	12
88	index_file	ParametricBranching:GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.496040	2020-07-09 01:30:16.779843	12
89	topology_file	ParametricBranching:GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.496037	2020-07-09 01:30:16.779840	12
90	data_file	ParametricBranching:GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.496033	2020-07-09 01:30:16.779836	12
91	mp4_file	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493255	2020-07-09 01:30:16.776401	12
92	surfactant_tail_rmsd	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493253	2020-07-09 01:30:16.776396	12
93	surfactant_head_rmsd	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493250	2020-07-09 01:30:16.776390	12
94	substrate_rmsd	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493248	2020-07-09 01:30:16.776384	12
95	counterion_rmsd	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493245	2020-07-09 01:30:16.776378	12
96	surfactant_tail_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493243	2020-07-09 01:30:16.776372	12
97	surfactant_head_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493240	2020-07-09 01:30:16.776366	12
98	surfactant_head_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493238	2020-07-09 01:30:16.776360	12
99	substrate_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493235	2020-07-09 01:30:16.776353	12
100	substrate_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493233	2020-07-09 01:30:16.776339	12
101	substrate_substrate_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493230	2020-07-09 01:30:16.776330	12
102	counterion_surfactant_tail_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493228	2020-07-09 01:30:16.776324	12
103	counterion_surfactant_head_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493225	2020-07-09 01:30:16.776322	12
104	counterion_substrate_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493223	2020-07-09 01:30:16.776319	12
105	counterion_counterion_rdf	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493220	2020-07-09 01:30:16.776317	12
106	data_file	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493218	2020-07-09 01:30:16.776314	12
107	trajectory_file	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493215	2020-07-09 01:30:16.776312	12
108	energy_file	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493213	2020-07-09 01:30:16.776309	12
109	log_file	ParametricBranching:GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.493208	2020-07-09 01:30:16.776303	12
110	restraint_file	ParametricBranching:GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.485513	2020-07-09 01:30:16.768394	12
111	topology_file	ParametricBranching:GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.485510	2020-07-09 01:30:16.768391	12
112	data_file	ParametricBranching:GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.485505	2020-07-09 01:30:16.768386	12
113	png_file	ParametricBranching:SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.477063	2020-07-09 01:30:16.759762	12
114	data_file	ParametricBranching:SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.477057	2020-07-09 01:30:16.759755	12
115	png_file	PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.359422	2020-07-09 01:30:15.359422	1
116	indenter_file	PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.359415	2020-07-09 01:30:15.359415	1
117	png_file	SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.326057	2020-07-09 01:30:15.326057	1
118	indenter_file	SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.326050	2020-07-09 01:30:15.326050	1
119	png_file	IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.276541	2020-07-09 01:30:15.276541	1
120	indenter_file	IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-09 01:30:15.276530	2020-07-09 01:30:15.276530	1
121	input	ParametricBranching:GromacsPullPrep:GromacsPullPrepMain:push_infiles	pull.mdp.template	None	None	1
122	indenter_file	IndenterBoundingSphere:IndenterBoundingSphereMain:push_infiles	AU_111_r_25.pdb	None	None	1
123	surfactant_file	SurfactantMoleculeMeasures:SurfactantMoleculeMeasuresMain:push_infiles	1_SDS.pdb	None	None	1
124	input	ParametricBranching:GromacsPullPrep:GromacsPullPrepMain:push_infiles	sys.top.template	None	None	1
125	input	ParametricBranching:GromacsNVTEquilibration:GromacsNVTEquilibrationMain:push_infiles	nvt.mdp	None	None	1
126	template	ParametricBranching:SphericalSurfactantPacking:push_infiles	sphere.inp.template	None	None	1
127	input	ParametricBranching:GromacsEnergyMinimization:GromacsEnergyMinimizationMain:push_infiles	em.mdp	None	None	1
128	input	ParametricBranching:GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles	renumber_png.sh	None	None	1
129	input	ParametricBranching:GromacsNPTEquilibration:GromacsNPTEquilibrationMain:push_infiles	npt.mdp	None	None	1
130	data	ParametricBranching:SphericalSurfactantPacking:push_infiles	1_NA.pdb	None	None	1
131	input	ParametricBranching:GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles	movie.pml.template	None	None	1
132	input	ParametricBranching:GromacsEnergyMinimizationAfterSolvation:GromacsEnergyMinimizationAfterSolvationMain:push_infiles	em_solvated.mdp	None	None	1

	type	name	earliest	latest	object_count
0	mp4_file	NaN	2020-07-09 01:30:15.493255	2020-07-09 01:30:16.776401	12
1	surfactant_tail_rmsd	NaN	2020-07-09 01:30:15.493253	2020-07-09 01:30:16.776396	12
2	surfactant_head_rmsd	NaN	2020-07-09 01:30:15.493250	2020-07-09 01:30:16.776390	12
3	substrate_rmsd	NaN	2020-07-09 01:30:15.493248	2020-07-09 01:30:16.776384	12
4	counterion_rmsd	NaN	2020-07-09 01:30:15.493245	2020-07-09 01:30:16.776378	12
5	surfactant_tail_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.493243	2020-07-09 01:30:16.776372	12
6	surfactant_head_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.493240	2020-07-09 01:30:16.776366	12
7	surfactant_head_surfactant_head_rdf	NaN	2020-07-09 01:30:15.493238	2020-07-09 01:30:16.776360	12
8	substrate_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.493235	2020-07-09 01:30:16.776353	12
9	substrate_surfactant_head_rdf	NaN	2020-07-09 01:30:15.493233	2020-07-09 01:30:16.776339	12
10	substrate_substrate_rdf	NaN	2020-07-09 01:30:15.493230	2020-07-09 01:30:16.776330	12
11	counterion_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.493228	2020-07-09 01:30:16.776324	12
12	counterion_surfactant_head_rdf	NaN	2020-07-09 01:30:15.493225	2020-07-09 01:30:16.776322	12
13	counterion_substrate_rdf	NaN	2020-07-09 01:30:15.493223	2020-07-09 01:30:16.776319	12
14	counterion_counterion_rdf	NaN	2020-07-09 01:30:15.493220	2020-07-09 01:30:16.776317	12
15	data_file	NaN	2020-07-09 01:30:15.493218	2020-07-09 01:30:16.776314	12
16	trajectory_file	NaN	2020-07-09 01:30:15.493215	2020-07-09 01:30:16.776312	12
17	energy_file	NaN	2020-07-09 01:30:15.493213	2020-07-09 01:30:16.776309	12
18	log_file	NaN	2020-07-09 01:30:15.493208	2020-07-09 01:30:16.776303	12

	type	name	earliest	latest	object_count
0	mp4_file	NaN	2020-07-09 01:30:15.514376	2020-07-09 01:30:16.787594	12
1	surfactant_tail_rmsd	NaN	2020-07-09 01:30:15.514373	2020-07-09 01:30:16.787591	12
2	surfactant_head_rmsd	NaN	2020-07-09 01:30:15.514371	2020-07-09 01:30:16.787589	12
3	substrate_rmsd	NaN	2020-07-09 01:30:15.514369	2020-07-09 01:30:16.787587	12
4	counterion_rmsd	NaN	2020-07-09 01:30:15.514366	2020-07-09 01:30:16.787584	12
5	surfactant_tail_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.514364	2020-07-09 01:30:16.787582	12
6	surfactant_head_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.514361	2020-07-09 01:30:16.787579	12
7	surfactant_head_surfactant_head_rdf	NaN	2020-07-09 01:30:15.514359	2020-07-09 01:30:16.787577	12
8	substrate_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.514356	2020-07-09 01:30:16.787574	12
9	substrate_surfactant_head_rdf	NaN	2020-07-09 01:30:15.514354	2020-07-09 01:30:16.787572	12
10	substrate_substrate_rdf	NaN	2020-07-09 01:30:15.514351	2020-07-09 01:30:16.787569	12
11	counterion_surfactant_tail_rdf	NaN	2020-07-09 01:30:15.514349	2020-07-09 01:30:16.787567	12
12	counterion_surfactant_head_rdf	NaN	2020-07-09 01:30:15.514346	2020-07-09 01:30:16.787565	12
13	counterion_substrate_rdf	NaN	2020-07-09 01:30:15.514344	2020-07-09 01:30:16.787562	12
14	counterion_counterion_rdf	NaN	2020-07-09 01:30:15.514342	2020-07-09 01:30:16.787560	12
15	topology_file	NaN	2020-07-09 01:30:15.514339	2020-07-09 01:30:16.787557	12
16	pullx_file	NaN	2020-07-09 01:30:15.514337	2020-07-09 01:30:16.787554	12
17	pullf_file	NaN	2020-07-09 01:30:15.514334	2020-07-09 01:30:16.787552	12
18	data_file	NaN	2020-07-09 01:30:15.514332	2020-07-09 01:30:16.787549	12
19	compressed_trajectory_file	NaN	2020-07-09 01:30:15.514329	2020-07-09 01:30:16.787547	12
20	trajectory_file	NaN	2020-07-09 01:30:15.514327	2020-07-09 01:30:16.787544	12
21	energy_file	NaN	2020-07-09 01:30:15.514324	2020-07-09 01:30:16.787525	12
22	log_file	NaN	2020-07-09 01:30:15.514319	2020-07-09 01:30:16.787520	12

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Pres-YX	Pres-YY	Pres-YZ	Pres-ZX	Pres-ZY	Pres-ZZ	#Surf*SurfTen	T-rest
nmolecules	step
306	0.0	0.0	124418.773438	73085.570312	11343.504883	7034.086914	81318.039062	-591056.3750	-122201.039062	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	1.0	1.0	123862.117188	73009.148438	11343.586914	7027.479004	81314.367188	-591071.7500	-122213.195312	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	2.0	2.0	123191.335938	72917.093750	11343.688477	7019.496094	81309.929688	-591090.1250	-122227.898438	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	3.0	3.0	122382.609375	72806.187500	11343.818359	7009.841309	81304.570312	-591112.1250	-122245.664062	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	4.0	4.0	121406.632812	72672.406250	11343.990234	6998.136230	81298.078125	-591138.4375	-122267.218750	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
131	9994.0	9994.0	203.646118	10514.221680	3445.140381	785.364929	33701.832031	-583234.1875	-84455.656250	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9995.0	9995.0	202.983170	10516.074219	3445.141357	784.984802	33701.546875	-583235.3750	-84455.593750	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9997.0	9997.0	202.686905	10514.833984	3445.142822	785.211243	33701.703125	-583234.8125	-84455.859375	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9998.0	9998.0	203.242203	10516.381836	3445.144531	784.940125	33701.468750	-583235.7500	-84456.125000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9999.0	9999.0	202.928757	10514.597656	3445.145508	785.273254	33701.699219	-583234.9375	-84456.484375	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

		Restraint-Pot.	Position-Rest.	COM-Pull-En.	Potential	Kinetic-En.	Total-Energy	Temperature	Pressure	Constr.-rmsd
nmolecules	time
306	0.0	7031.586914	0.000000	0.001686	-671124.8750	58.651764	-671066.2500	0.523988	3026.758545	0.000002
	0.2	399.287201	163.539291	263.659607	-675284.5625	4311.048828	-670973.5000	38.514393	2849.341309	0.000002
	0.4	407.408813	368.645813	480.917938	-676434.0625	5720.614258	-670713.4375	51.107281	3003.247803	0.000002
	0.6	461.685181	814.757324	902.301514	-676609.6250	6233.531250	-670376.1250	55.689621	3057.637451	0.000002
	0.8	441.735321	1231.140625	1313.032349	-676190.5000	6247.245117	-669943.2500	55.812134	3064.695801	0.000002
...	...	...	...	...	...	...	...	...	...	...
44	1.2	27.327681	166.368652	187.659424	-587116.6250	1162.453857	-585954.1875	72.272461	2990.296631	0.000002
	1.4	32.520138	265.443604	170.982346	-587168.5625	1249.532227	-585919.0000	77.686325	2977.302490	0.000002
	1.6	33.898804	334.627075	174.498428	-587258.0000	1362.636475	-585895.3750	84.718277	2962.027344	0.000002
	1.8	56.819633	380.152008	170.386108	-587446.0625	1566.714966	-585879.3750	97.406311	2958.099609	0.000002
	2.0	58.596825	379.724152	165.710358	-587490.4375	1629.716431	-585860.7500	101.323257	2970.756348	0.000003

	nmolecule	0										...	524
	coord	1	1 ref	1 dX	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	1 g 2 X	1 g 2 Y	...	1 ref	1 dX	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	1 g 2 X	1 g 2 Y	1 g 2 Z
nmolecules	time
306	0.0	2.99688	2.99656	2.234560	-1.98988	0.168659	6.785	6.759	6.790	9.01956	4.76911	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.98128	3.01656	2.212180	-1.99218	0.159762	6.785	6.759	6.790	8.99718	4.76682	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.96838	3.03656	2.215250	-1.97014	0.149943	6.785	6.759	6.790	9.00025	4.78885	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.94686	3.05656	2.203310	-1.94835	0.182654	6.785	6.759	6.790	8.98831	4.81065	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.92555	3.07656	2.171630	-1.94456	0.248140	6.785	6.759	6.790	8.95663	4.81444	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
131	1.2	3.14826	3.21024	-0.853757	-1.51078	2.626820	6.785	6.624	6.675	5.93125	5.11321	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	3.16664	3.23024	-0.903389	-1.49367	2.642060	6.785	6.624	6.675	5.88162	5.13033	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	3.17031	3.25024	-0.941155	-1.46587	2.648840	6.785	6.624	6.675	5.84385	5.15812	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	3.19711	3.27024	-0.989481	-1.42622	2.684830	6.785	6.624	6.675	5.79552	5.19778	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	3.20565	3.29024	-1.002790	-1.41113	2.698030	6.785	6.624	6.675	5.78222	5.21286	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

coord	nmolecules	time	1	1 dX	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	1 g 2 X	1 g 2 Y	1 g 2 Z	1 ref
0	306	0.0	3.030811	-0.003623	0.015265	0.000827	6.785	6.759	6.790	6.781381	6.774262	6.790828	3.030759
1	306	0.2	3.016804	-0.005029	0.012748	-0.000155	6.785	6.759	6.790	6.779975	6.771745	6.789846	3.050759
2	306	0.4	3.023511	-0.003008	0.012060	-0.001445	6.785	6.759	6.790	6.781996	6.771057	6.788555	3.070759
3	306	0.6	3.027459	-0.002068	0.012372	-0.001094	6.785	6.759	6.790	6.782937	6.771369	6.788907	3.090759
4	306	0.8	3.035076	-0.000224	0.013153	-0.000372	6.785	6.759	6.790	6.784780	6.772150	6.789629	3.110759
...	...	...	...	...	...	...	...	...	...	...	...	...	...
127	131	1.2	3.111250	0.021083	0.208217	0.162973	6.785	6.624	6.675	6.806087	6.832213	6.837974	3.157227
128	131	1.4	3.133500	0.015456	0.202484	0.163395	6.785	6.624	6.675	6.800462	6.826481	6.838397	3.177227
129	131	1.6	3.162940	0.011403	0.199794	0.164100	6.785	6.624	6.675	6.796407	6.823791	6.839101	3.197227
130	131	1.8	3.184166	0.011572	0.192103	0.161455	6.785	6.624	6.675	6.796577	6.816100	6.836456	3.217227
131	131	2.0	3.200362	0.016526	0.188083	0.161090	6.785	6.624	6.675	6.801531	6.812080	6.836091	3.237227

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	Coul. recip.	Position Rest.	...	Pres-XY	Pres-XZ	Pres-YX	Pres-YY	Pres-YZ	Pres-ZX	Pres-ZY	Pres-ZZ	#Surf*SurfTen	T-rest
nmolecules	step
525	0.0	0.0	161.804901	43983.476562	15446.817383	3762.690674	135706.421875	958655.812500	-3724685.75	40462.300781	0.262039	...	56.934471	72.266235	56.934483	-273.142059	-24.399984	72.266251	-24.399986	-253.475372	0.0	0.0
	1.0	1.0	161.778137	43983.011719	15446.825195	3762.915039	135706.828125	956965.562500	-3724699.25	40462.062500	0.262045	...	56.815948	72.130508	56.815948	-273.195740	-24.467348	72.130516	-24.467337	-253.646317	0.0	0.0
	2.0	2.0	161.758942	43982.449219	15446.833008	3763.183350	135707.296875	954930.562500	-3724715.75	40461.687500	0.262154	...	56.673618	71.968033	56.673630	-273.259247	-24.548172	71.968018	-24.548176	-253.849869	0.0	0.0
	3.0	3.0	161.752808	43981.742188	15446.843750	3763.515381	135707.890625	952477.187500	-3724736.25	40461.351562	0.262432	...	56.502007	71.772667	56.501995	-273.335602	-24.643551	71.772652	-24.643547	-254.094406	0.0	0.0
	4.0	4.0	161.778061	43980.878906	15446.851562	3763.920898	135708.625000	949522.125000	-3724761.00	40460.863281	0.262990	...	56.294838	71.538246	56.294838	-273.426453	-24.755783	71.538223	-24.755787	-254.388443	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
219	9995.0	9995.0	261.282990	16753.611328	5973.765137	1430.592163	56566.898438	-110296.773438	-4553745.00	5788.273438	2115.128174	...	2.728610	13.643753	2.728613	-396.830353	-8.665934	13.643753	-8.665941	-391.928040	-0.0	0.0
	9996.0	9996.0	263.644775	16757.851562	5973.758301	1431.567261	56567.769531	-110295.820312	-4553756.50	5787.473145	2115.128662	...	2.728566	13.644175	2.728566	-396.830719	-8.666225	13.644176	-8.666223	-391.929749	-0.0	0.0
	9998.0	9998.0	256.603333	16754.261719	5973.758301	1430.978394	56567.246094	-110294.570312	-4553755.50	5787.886719	2115.116943	...	2.728557	13.644362	2.728555	-396.830872	-8.666327	13.644370	-8.666327	-391.930603	-0.0	0.0
	9999.0	9999.0	266.869904	16759.068359	5973.746582	1431.640015	56567.847656	-110289.031250	-4553779.00	5787.276367	2115.112793	...	2.728649	13.645870	2.728647	-396.833008	-8.667096	13.645872	-8.667089	-391.936920	-0.0	0.0
	10000.0	10000.0	257.433350	16753.835938	5973.747559	1430.834473	56567.125000	-110287.750000	-4553777.00	5787.891113	2115.098877	...	2.728636	13.646180	2.728632	-396.833466	-8.667222	13.646176	-8.667225	-391.937836	-0.0	0.0

	nmolecules	name	earliest	latest	object_count
0	525	NaN	2020-07-09 01:30:16.798142	2020-07-09 01:30:16.798142	1
1	481	NaN	2020-07-09 01:30:16.697143	2020-07-09 01:30:16.697143	1
2	438	NaN	2020-07-09 01:30:16.585689	2020-07-09 01:30:16.585689	1
3	394	NaN	2020-07-09 01:30:16.375198	2020-07-09 01:30:16.375198	1
4	350	NaN	2020-07-09 01:30:16.288551	2020-07-09 01:30:16.288551	1
5	306	NaN	2020-07-09 01:30:16.201223	2020-07-09 01:30:16.201223	1
6	263	NaN	2020-07-09 01:30:16.116890	2020-07-09 01:30:16.116890	1
7	219	NaN	2020-07-09 01:30:16.024390	2020-07-09 01:30:16.024390	1
8	175	NaN	2020-07-09 01:30:15.907847	2020-07-09 01:30:15.907847	1
9	131	NaN	2020-07-09 01:30:15.792477	2020-07-09 01:30:15.792477	1
10	88	NaN	2020-07-09 01:30:15.678061	2020-07-09 01:30:15.678061	1
11	44	NaN	2020-07-09 01:30:15.524564	2020-07-09 01:30:15.524564	1

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	Coul. recip.	Potential	...	Pres-YY	Pres-YZ	Pres-ZX	Pres-ZY	Pres-ZZ	#Surf*SurfTen	T-non-Substrate	T-Substrate	Lamb-non-Substrate	Lamb-Substrate
nmolecules	step
88	0.0	0.0	131.851639	6730.247559	2331.482422	563.887268	22769.750000	-90148.273438	-4492901.50	4877.040039	-4545645.50	...	-569.832947	149.611359	52.919952	148.824631	212.112366	6601.140625	298.622192	0.0	1.0	1.0
	1.0	1.0	1162.473389	11973.799805	3137.247070	828.917114	22561.390625	-158159.500000	-4016967.75	7517.374023	-4127946.00	...	1440.258789	8.070938	-94.850601	9.854258	1540.382324	778.841431	205.794357	0.0	1.0	1.0
	2.0	2.0	1367.311646	12871.199219	3307.925781	840.347046	22467.876953	-180193.906250	-3907587.00	8363.152344	-4038563.00	...	1919.452881	-15.313110	-78.789360	-14.358397	1850.456299	-651.079895	236.390549	0.0	1.0	1.0
	3.0	3.0	1506.104248	13498.419922	3386.662109	863.888062	22389.306641	-201827.812500	-3819601.50	9039.631836	-3970745.25	...	2053.746094	17.369396	-8.322670	17.652386	1992.019531	-628.092163	253.358810	0.0	1.0	1.0
	4.0	4.0	1578.914062	13755.295898	3405.137451	981.144836	22575.958984	-218306.187500	-3755306.25	9326.000977	-3921990.00	...	2065.312500	85.177956	-14.530103	85.717766	2235.899902	1905.061157	265.853516	0.0	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
131	96.0	96.0	2275.751221	20615.474609	5111.305176	1220.233032	32238.833984	-265550.625000	-3621643.75	10956.721680	-3814776.00	...	2340.132568	-36.568039	-6.411274	-35.111465	2234.196533	-1246.248047	298.351807	0.0	1.0	1.0
	97.0	97.0	2320.255127	20566.779297	5227.749512	1334.061279	32228.570312	-263319.656250	-3623626.50	10830.979492	-3814437.75	...	2268.215820	72.517715	-19.330690	73.329025	2445.256836	1180.723755	297.676331	0.0	1.0	1.0
	98.0	98.0	2226.532471	20639.490234	5193.020996	1322.358398	32075.978516	-260981.781250	-3627749.25	10922.756836	-3816351.00	...	2449.972412	-16.837786	-18.271149	-17.778309	2501.166016	1467.692139	298.807434	0.0	1.0	1.0
	99.0	99.0	2347.639648	20508.808594	5255.947754	1347.232422	32205.826172	-261680.937500	-3626631.00	10918.868164	-3815727.50	...	2467.057373	92.324211	7.327162	92.179932	2457.091064	399.910370	298.549377	0.0	1.0	1.0
	100.0	100.0	2264.749756	21071.445312	5265.748047	1268.623535	32162.523438	-260976.312500	-3627749.00	10772.904297	-3815919.25	...	2418.405029	3.469247	25.110744	3.525263	2396.902588	-30.178104	298.543121	0.0	1.0	1.0

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	Coul. recip.	Potential	...	Coul-14:Ion-SOL	LJ-14:Ion-SOL	Coul-SR:SOL-SOL	LJ-SR:SOL-SOL	Coul-14:SOL-SOL	LJ-14:SOL-SOL	T-non-Substrate	T-Substrate	Lamb-non-Substrate	Lamb-Substrate
nmolecules	step
219	0.0	0.0	3898.649658	34585.683594	8730.751953	2178.489746	54013.085938	-270168.53125	-3663265.75	11030.066406	-3818997.75	...	0.0	0.0	-3425929.25	343344.12500	0.0	0.0	299.224976	0.0	1.0	1.0
	1.0	1.0	3940.612549	34278.667969	8648.569336	2173.595459	54150.582031	-287802.15625	-3643330.00	11169.256836	-3816770.75	...	0.0	0.0	-3405750.75	332319.96875	0.0	0.0	295.258850	0.0	1.0	1.0
	2.0	2.0	3895.102295	34878.105469	8620.577148	2206.588135	53995.351562	-295437.25000	-3624556.50	9600.658203	-3806797.50	...	0.0	0.0	-3385145.50	330405.46875	0.0	0.0	293.966614	0.0	1.0	1.0
	3.0	3.0	3868.743164	34329.398438	8576.867188	2203.896240	53912.531250	-293934.68750	-3612464.50	11589.123047	-3791918.75	...	0.0	0.0	-3375569.25	331836.43750	0.0	0.0	293.817139	0.0	1.0	1.0
	4.0	4.0	3718.966797	34757.335938	8675.570312	2211.027832	53979.644531	-287169.50000	-3617197.50	11339.883789	-3789684.50	...	0.0	0.0	-3379453.75	338639.03125	0.0	0.0	295.376831	0.0	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
481	96.0	96.0	8694.218750	76607.304688	18665.460938	4800.224609	116442.460938	-309848.21875	-3711858.00	11349.335938	-3785147.25	...	0.0	0.0	-3191982.50	323291.21875	0.0	0.0	298.991974	0.0	1.0	1.0
	97.0	97.0	8225.478516	76410.453125	18893.029297	4684.645020	116295.460938	-312296.71875	-3706866.50	11178.047852	-3783476.25	...	0.0	0.0	-3187694.50	321779.18750	0.0	0.0	298.237335	0.0	1.0	1.0
	98.0	98.0	8371.156250	76153.546875	18816.169922	4809.505859	116276.585938	-310113.09375	-3710946.50	11268.166016	-3785364.50	...	0.0	0.0	-3191932.00	323418.87500	0.0	0.0	298.616394	0.0	1.0	1.0
	99.0	99.0	8265.072266	76100.406250	18867.687500	4719.337402	116454.460938	-307491.00000	-3714744.75	11171.698242	-3786657.00	...	0.0	0.0	-3195261.00	325307.46875	0.0	0.0	298.921967	0.0	1.0	1.0
	100.0	100.0	8455.542969	76583.679688	19121.949219	4731.532715	116297.132812	-309148.28125	-3714432.00	11381.548828	-3787008.75	...	0.0	0.0	-3194282.75	323696.25000	0.0	0.0	298.601868	0.0	1.0	1.0